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Do political and economic decision-making rely on
common neural substrates?
Sekoul Krastev
Integrated Program in Neuroscience
McGill University, Montréal
August 2015
A thesis submitted to McGill University in partial fulfillment of the requirements
of the degree of Masters of Science
© Sekoul Krastev 2015
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TABLE OF CONTENTS
ABSTRACT ....................................................................................................................... 3
RÉSUMÉ............................................................................................................................ 5
PREFACE.......................................................................................................................... 7
CHAPTER 1: INTRODUCTION .................................................................................... 8
CHAPTER 2: Do political and economic choices rely on common neural substrates?
A review and fMRI meta-analysis. ................................................................................ 12
BACKGROUND.......................................................................................................... 12
METHODS .................................................................................................................. 14
Lite rature Search ..................................................................................................... 14
Are there domain general neural responses common to subjective value in
economic and political contexts? ............................................................................ 14
Comparison of Gene ralize d and Political SV Correlates..................................... 15
RESULTS..................................................................................................................... 16
DISCUSSION .............................................................................................................. 23
CHAPTER 3: Are decision behaviors similar in economic and political choice?..... 27
BACKGROUND.......................................................................................................... 27
Evidence Gathering during Binary Choice ........................................................... 29
Attentional Drift Diffusion Model .......................................................................... 30
Specific Aims ............................................................................................................ 31
Predictions.................................................................................................................... 33
METHODS .................................................................................................................. 34
Subjects ..................................................................................................................... 34
Materials ................................................................................................................... 34
Eye-Tracking ............................................................................................................ 35
Procedure.................................................................................................................. 35
Data Analysis............................................................................................................ 38
RESULTS..................................................................................................................... 39
Demographic Results ............................................................................................... 39
Choice Task Results................................................................................................. 40
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Simulation................................................................................................................. 49
DISCUSSION .............................................................................................................. 53
Alte rnative Decision Models ................................................................................... 57
Conclusion & Future Directions............................................................................. 58
CHAPTER 4: GENERAL DISCUSSION & CONCLUSION .................................... 60
ACKNOWLEDGEMENTS ........................................................................................... 64
REFERENCES................................................................................................................ 65
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ABSTRACT
The methods of cognitive neuroscience have begun to be applied to study political
behavior. The neural substrates of value-based choice have already been extensively
studied in economic contexts, and might provide a powerful starting point for
understanding political choice. In this thesis, I present work that addresses the
commonalities and distinctions between political and economic choice, within a cognitive
neuroscience framework. First, a systematic literature review was undertaken to identify
papers reporting neural correlates of political behavior in humans. We then asked
whether the brain regions linked to subjective value in economic choice were engaged
during political choice, addressing this question with a functional magnetic resonance
imaging meta-analysis. This showed that only a small number of studies of political
behavior have used frameworks that are comparable to those used in neuroeconomics.
Further, few of the activation foci reported in these studies of political behavior fell
within areas consistently found to reflect subjective value in economic studies. This
raised the interesting possibility that the neural substrates of subjective value identified in
economic choice paradigms may not generalize to political choice, but also highlighted
the need for political choice paradigms that would allow this question to be directly
tested.
As a first step in this direction, in a second study we adapted a task commonly used to
study information gathering in economic choice to study hypothetical voting choices. We
asked whether this methodology could be applied to measure evidence gathering in
voting and explored the effect of partisanship on this process. Twelve Canadian Liberal
partisans and twelve non-partisans made binary choices between photographs of
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unknown political candidates in the presence and absence of party information, while
choice behavior and eye movements were measured. In the absence of party information,
we found that choice behavior and eye movement patterns across groups resembled those
found in economic choice studies, but partisans trended toward faster choices and made
significantly fewer fixations. When party information was introduced, both groups still
conformed to choice behavior and eye movement patterns consistent with those seen in
economic paradigms. Although party information had a substantial effect on voting
behavior and eye movements in partisans, it did not completely supersede the effects of
visual information and attentional modulation present throughout a trial as would have
been expected if choices were being made purely based on party information. Preliminary
efforts to fit an existing computational model developed in economic choice showed that
partisans’ behavior was consistent with a lowered decision threshold, and party
information acted to boost the initial value of the option for partisans. This work suggests
that binary choice tasks used in economic studies can be applied to analyze political
decisions, and provides preliminary data on the mechanisms by which partisanship
influences such choices. This thesis provides a starting point for a neuroscience-informed
analysis of political decision-making behaviors, and sets the stage for work to address the
neural basis of these behaviors.
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RÉSUMÉ
Les méthodes de la neuroscience cognitive ont commencé à être appliquées pour étudier le
comportement politique. Les substrats neuronaux des choix de valeur ont déjà été largement étudié
dans des contextes économiques, et pourraient fournir un point de départ pour comprendre les choix
politiques. Dans ce document, je présente des travaux portant sur les points communs et les
différences entre le choix politique et économique, dans le cadre de la neuroscience cognitive. Tout
d'abord, une analyse systématique de la littérature a été effectuée afin d'identifier les publications
rapportant des corrélats neuraux de comportements politiques chez les humains. Nous avons ensuite
demandé si les régions du cerveau liées à la valeur subjective dans les choix économiques ont été
engagées au cours des choix politiques. Pour répondre à cette question, nous avons effectué une
méta-analyse et les résultats ont suggéré qu’un nombre relativement faible d'études de
comportement politique ont rapporté des régions du cerveau qui sont comparables à ceux utilisés
dans les taches neuroéconomiques. En outre, peu des régions d’activation rapportées dans ces
études de comportement politique ont tombées dans les zones liées à la valeur subjective dans les
études économiques. Cela soulève la possibilité que les substrats neuronaux identifiés dans les
paradigmes de choix économique ne peuvent pas être généralisés aux choix politiques. Il démontre
également le besoin pour des paradigmes expérimentaux qui peuvent être comparés aux recherches
existantes sur les choix économiques.
Dans une deuxième étude, nous avons adapté une tâche de choix binaire couramment utilisé dans
l’étude des choix économiques pour étudier les choix politiques. Nous avons demandé si cette
méthode peut être appliquée pour mesurer la collecte de données pendant les choix politiques et
ensuite nous avons exploré l'effet de la partisannerie sur ce processus. Douze partisans libéraux
canadiens et douze non-partisans ont fait des choix binaires entre des photographies de candidats
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politiques inconnus en présence et en absence d'informations du parti, tandis que les comportements
de choix et les mouvements oculaires choix ont été mesurés. En absence d'information sur la partie
politique des candidats, nous avons constaté que les comportements de choix et les mouvements
oculaires entre les groupes ressemblaient à ceux retrouvés dans les études de choix économiques.
De plus, nous avons observé une tendance vers des choix plus rapides et un plus grand nombre de
fixations visuels dans le groupe partisan. Lorsque l'information du parti politique a été introduit, les
deux groupes conformaient encore aux comportements de choix et les mouvements oculaires. Bien
que l'information du parti a eu un effet considérable sur le comportement de choix et les
mouvements oculaires des partisans, il n’a pas remplacé complètement les effets de l'information
visuel ainsi que la modération attentionnelle présente tout au long du procès – quelque chose qui
aurait pu être attendu si les choix étaient faits purement basée sur l'information de partie. Des efforts
préliminaires pour adapter nos données à un modèle existant développé dans le contexte des choix
économiques ont montré que le comportement partisan est compatible avec une barrière de décision
réduite, et que l'information du parti agit pour augmenter la valeur initiale de d’une option. Ce
document suggère que les tâches de choix binaires utilisés dans les études de choix économiques,
ainsi que les modèles analytiques développées dans ces contextes, peuvent être généralisés aux
décisions politiques. Ensuite, il fournit des données préliminaires sur les mécanismes par lesquels la
partisannerie influence de tels choix. Cette dissertation fournit un point de départ pour une analyse
des comportements de prise de décisions politiques informé par la méthode neuroscientifique, et
ouvre la voie pour aborder la question de la base neurale de ces comportements.
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PREFACE
All of the research presented in this thesis was carried out at the Montreal
Neurological Institute in Montreal, Quebec, under the supervision of Dr. Lesley K.
Fellows with advice on the political science elements from Dr. Dietlind Stolle and Dr.
Elisabeth Gidengil, Dept. of Political Science. The literature review presented in Chapter
2 was carried out by Sekoul Krastev and the fMRI meta-analysis was carried out by
Sekoul Krastev with the help of Dr. Joe Kable and, Dr. Joe McGuire at the University of
Pennsylvania. The task in Chapter 3 was adapted from work at Dr. Antonio Rangel’s
laboratory at the California Institute of Technology with the guidance of Dr. Ian
Krajbich. The political choice task and trial generation scripts were adapted by Sekoul
Krastev from work on an aesthetic choice task created by Avinash Vaidya. Subject
recruitment was done by Sekoul Krastev in collaboration with Dr. Dietlind Stolle and Dr.
Elisabeth Gidengil, using a database from the Center for the Study of Democratic
Citizenship at McGill University. Additional subjects were recruited from the community
with the help of Christine Déry.
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CHAPTER 1: INTRODUCTION
Political behavior is a complex form of social interaction, ubiquitous across
human societies and ecologically instrumental for outcome generation within groups
(Schreiber, 2004). Although political behavior has traditionally been studied at the group
level, it emerges from decisions taken by individuals (Sniderman et al., 1993). An
increasing focus on the individual in political science has given rise to the field of
political psychology, which seeks to explore the role of factors such as emotion,
personality, socialization, group association and conflict in political behaviours (Cottam
et al., 2010). As political theories have increasingly benefited from psychological models
of behavior, researchers have begun to take an interest in their biological underpinnings
(Lieberman et al., 2003; Oxley et al., 2008; McDermott, 2009). During the last decade, a
small body of research has emerged, using the methods of cognitive neuroscience to
study political behavior.
Since the primary goal of political behavior is the distribution of decision-making
power to members of a group, it is, by definition, a decision behavior. The neural
substrates of decision-making have been extensively studied in economic contexts and
might provide a powerful starting point for investigating the biological basis of political
choice. This effort, sometimes termed “neuroeconomics”, has been deeply influenced by
the same neoclassical economic models of utility that have shaped discussions of political
decision-making within rational choice theories of political behavior. To the extent that
political choices also involve weighing options with the goal of maximizing subjective
value (utility), this neuroeconomic framework may also be relevant for studying the brain
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basis of political decision-making contexts (Fellows, 2004; Kable & Glimcher, 2009;
Rangel et al., 2008; Padoa-Schioppa, 2011; Rangel & Clithero, 2013).
As in economic contexts, deliberative political choice is a cognitively taxing
undertaking (Downs, 1957; Fiorina, 1981): in principle, a decision-maker attempts to
collect and process all currently available information in order to rank alternatives based
on their expected utility. Decisions are thought to involve preference formation relying
on an online comparison of the values assigned to competing options (Padoa-Schioppa,
2011). While several models have been developed to explain how this happens in the
brain, largely based on economic paradigms, it seems likely that multiple computational
steps are involved: option identification, value assignment, action selection, outcome
evaluation and learning (Fellows, 2004). The assignment of subjective value to options
lies at the conceptual center of the decision-making process in this type of model and has
been the focus of much of the decision neuroscience work to date (Fellows, 2004; Kable
& Glimcher, 2009; Rangel et al., 2008; Padoa-Schioppa, 2011).
While deliberative decision-making may have normative appeal, the motivation
and cognitive work required often eclipse the amount of effort most citizens are willing to
invest in political affairs (Fiske & Taylor, 1991). In an alternative model, decisionmaking can instead be driven by the use of ‘heuristics’ or cognitive shortcuts (Lupia et
al., 2000). In this case, the decision process may consist of little more than the
identification of alternatives and the use of simple rules of thumb to make a choice: for
example, using a candidate’s party affiliation to infer issue positions (Rahn, 1993).
A similar version of this deliberative – heuristic dichotomy categorizes political
decision-making strategies in terms of the degree of cognitive effort involved in
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collecting and processing relevant information. This model proposes a distinction
between memory-based information processing and impression driven on-line processing
in making political judgments (Lodge et al., 1989). In the former, decisions are made by
recalling from long-term memory all of the available considerations that surround an
issue or candidate. By weighing each consideration against the other, a rough balance of
positive and negative aspects can be computed that serves as a summary judgment. For
impression driven on-line processing, on the other hand, new information is judged as
positive or negative upon contact and then integrated into an existing ‘judgment tally’
that summarizes previous encounters with the object under consideration. Political
scientists disagree on which processes underlie political decision-making.
Neuroscience
evidence may be helpful here as different decision processes likely rely on distinct neural
circuits.
The present thesis takes initial steps in identifying overlaps between the
conceptual, behavioral and neural substrates of economic and political decision-making.
In Chapter 2, I present the results of a systematic review of the neuropolitics literature to
date, with the results of the relevant subset of fMRI studies of political choice
summarized quantitatively in relation to regions commonly associated with subjective
value signaling in economic choice. In Chapter 3, I report a behavioral experiment in
which I adapt a binary choice task widely used to study information gathering in
economic choice contexts to study political choice. I compare choice and eye movement
behaviors in this laboratory voting paradigm with patterns reported in economic versions
of this task, ask whether these patterns can be mimicked by a model of attentionmediated evidence gathering, and explore the impact of providing political party
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information to Liberal partisan and non-partisan participants. The work presented in this
thesis sets the stage for studies of the neural basis of political decision-making. I provide
evidence as to whether political and economic decisions rely on common processes, and a
starting point for a more comprehensive brain-based understanding of decision-making
that is able to accommodate decisions in both contexts.
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CHAPTER 2: Do political and economic choices rely on common neural
substrates? A review and fMRI meta-analysis.
BACKGROUND
Political choice is multifaceted and likely relies on a variety of decision-making
mechanisms. However, in most cases a political choice is likely one where some sort of
value is being computed. Thus, it would be useful to know to what extent the
neuroeconomics framework is applicable to the study of political decision-making.
Current neuroeconomic models propose that the assignment of value to alternatives likely
involves a set of variables that represent internal (e.g. hunger) and external (e.g. cost)
states relevant to the consequences of each option. There is converging evidence that the
orbitofrontal cortex (OFC), ventromedial prefrontal cortex (vmPFC), and associated
ventral striatum are involved in assigning subjective value to alternatives. Functional
neuroimaging studies in humans have shown value-related signals in vmPFC and ventral
striatum in a wide range of paradigms (Bartra et al., 2013), ventromedial frontal lesions
in humans disrupt even simple value based preferences (Fellows, 2007; Henri- Bhargava
et al., 2012), and electrophysiological studies in monkeys show that activity of neurons in
the OFC reflect changes in stimulus value (reviewed in Padoa-Schioppa (2011).
The same brain substrates seem to be involved in valuing a range of primary and
secondary rewards (e.g. money, odors, food, pleasurable music, attractive faces, and
social rewards). For example, Lin and colleagues (2012) found that social and monetary
decisions are associated with fMRI activation in overlapping regions of human vmPFC.
Similarly, Watson and Platt (2012) have found that OFC neurons encode both social and
non-social rewards in non-human primates. Pegors and colleagues (2014) found common
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activation of the vmPFC in response to both place and face attractiveness, with certain
areas of the vmPFC responding only to place attractiveness and others only to face
attractiveness. These findings suggest that value across many stimuli may be encoded, at
least in part, in a “common currency” in the vmPFC. Consistent with this claim, a recent
meta-analysis of 206 fMRI studies found a consistent set of regions where activation was
related to subjective value (SV) across a range of paradigms, involving primary or
secondary rewards, in social, economic, and aesthetic contexts (Bartra et al, 2013).
Existing findings regarding the neural substrates of value-based choice are a strong
potential starting point for the study of political choice. If, as the above presented results
suggest, the same neural circuitry is involved in computing value across different
modalities, then it is possible that these regions are also engaged in political decisionmaking. This possibility leads us to ask the following questions:
1. To what extent has a neuroeconomic framework been used to study the
neural substrates of political choice?
2. Are the same neural regions which are engaged during economic decisionmaking tasks also engaged during comparable political choice tasks?
As a first step in addressing these questions, we systematically reviewed the neuropolitics
literature, asking which existing studies used designs that were sufficiently similar to the
neuroeconomics literature to allow comparison. The results of fMRI studies that met
those criteria were summarized using a quantitative meta-analysis to provide preliminary
insights into whether there are common activation patterns related to subjective value
across these two literatures.
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METHODS
Literature Search
A systematic literature search was conducted to identify papers discussing the
neural correlates of political behavior in humans. The search terms “politics”, “political”,
“democrat”, “republican”, “brain” and “neuroscience” were used on Google Scholar and
Web of Knowledge. The database searches were supplemented by manual review of the
citations in these papers. Papers were included in the first stage of the literature review if
their central focus was the link between political behavior and the brain, whether review
articles or experimental studies. This yielded twenty-seven papers using various
techniques to study the brain correlates of a range of processes including face judgment
in
political contexts,
partisanship,
motivated
reasoning,
political interest,
political
attitudes and automatic processing of political preference.
Are there domain general neural responses common to subjective value in economic
and political contexts?
In a second step, we applied the same inclusion and exclusion criteria as a recent
meta-analysis of 206 fMRI studies investigating value-based choice (Bartra et al., 2013)
to this body of neuropolitics research. The reference meta-analysis included studies
containing the keywords “fMRI” and “reward” and identified brain regions where
activity was consistently related to behavioral measures of positive and negative SV
across a wide variety of value-based decision-making tasks; none of the neuropolitics
papers identified in the present search were included in that meta-analysis.
Subjective
value (SV), defined as the “common-currency” value attributed to available alternatives,
was either directly measured through ratings or preference judgments or, in the case of
monetary rewards, inferred a priori as being higher for larger amounts of money.
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The same criteria applied to our sample of neuropolitics literature yielded
English-language papers in which BOLD signal was measured with fMRI, as a function
of positive and/or negative SV. As in Bartra et al. (2013), we did not limit ourselves to
studies that used particular tasks or stimuli, instead accepting any experimental design
that yielded a clear behavioral measure of SV, such as voting for a candidate based on a
photograph of his/her face (positive SV) or rating a policy negatively on a visual analog
scale (negative SV). Only experiments that used whole brain analyses to report peak
activation foci in stereotactic spatial coordinates (Talairach or MNI space) and linked
them to either positive or negative SV measures were included.
The results of the fMRI studies that met these criteria were summarized in a
whole-brain meta-analysis. Talairach coordinates were converted to MNI space and
activation foci were coded according to whether they corresponded to positive or
negative SV. The list of coordinates so-identified is those voxels that showed
significantly increased BOLD signal in relation to a behavioral measure of either higher
or lower subjective value in a political task. A map of gray-matter probability (pGM)
values
(derived
from
the
ICBM
Tissue
Probabilistic
Atlases;
http://www.loni.ucla.edu/ICBM) was generated to test whether there was a consistent
pattern of activation related to positive or negative SV in political decision-making tasks
across this set of studies, with the null hypothesis that foci were distributed randomly in
the brain.
Comparison of Generalized and Political SV Correlates
Next, we tested whether activation foci from the neuropolitics studies fell within
the regions previously identified as consistently relating to SV in economic paradigms
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(Bartra et al., 2013). An ROI was established for positive and negative SV based on the
published meta-analysis. A mask was then generated in FSL (Smith et al., 2004); the
coordinates that remained represented the overlap between the political SV foci and the
SV regions reported in the Bartra et al. meta-analysis. For visualization purposes, a 5 mm
radius sphere was centered on each coordinate passed through the masking phase. Since
the effects in the Bartra et al. study did not show lateralization, and laterality was not
tested in the source studies for political SV, data were collapsed across hemispheres.
RESULTS
The initial literature search yielded 27 papers (Table 1) that corresponded to our
search terms and inclusion criteria. Importantly, none of the studies contained the
keyword “reward” that was present in all 200 of the Bartra et al. studies. Of these 27
papers, ten were reviews, six focused on political attitudes and emotion, four on face
judgment, three on party identity, three on automatic processing, two on political interest
and two on motivated reasoning. Overall, this analysis shows that neuropolitics is still an
emerging field with relatively few original research reports to date (Fowler and Schreiber
2008).
Table 1: Neuropolitics literature identified by search
Author
Year
Jost &
Amodio
2012
Kanai et al.
2011
Oxley et al.
2008
Dawes &
Fowler
2009
Title
Political ideology as Motivated Social
Cognition: Behavioral and Neuroscientific
Evidence
Political Orientations Are Correlated with
Brain Structure in Young Adults
Political Attitudes Vary with Physiological
Traits
Partisanship, Voting, and the Dopamine D2
Receptor Gene
Topic
Partisanship Motivated
Reasoning
Partisanship - Party
ID
Attitude
Partisanship Interest
Method
Structural MRI
Structural MRI
Physiological
Response
Genetics
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Implicit and Explicit Evaluation: fMRI
Correlates of Valence, Emotional Intensity,
and Control in the Processing of Attitudes
Interest in Politics Modulates Neural
Activity in the Amygdala and Ventral
Striatum
Us versus Them: Political Attitudes and
Party Affiliation Influence Neural Response
to Faces of Presidential Candidates
Neural Correlates of Attitude Change
Following Positive and Negative
Advertisements
Cunningham
et al.
2004
Gozzi et al.
2010
Kaplan et
al.
2007
Kato et al.
2009
Knutson et
al.
2006
Politics on the brain: An fMRI Investigation
Face Judgment
Rule et al.
2010
Voting Behavior Is Reflected In Amygdala
Response across Cultures
Face Judgment
Schreiber et
al.
2013
Spezio et al.
2009
Tusche et
al.
2013
Westen et
al.
2006
Zamboni et
al.
2009
Amodio et
al.
2007
Dhont et al.
2011
Fowler &
Schreiber
Friend &
Thayer
2008
2011
Red Brain, Blue Brain: Evaluative Processes
Differ in Democrats and Republicans
A neural basis for the effect of candidate
appearance on election outcomes
Automatic processing of political
preferences in the human brain
Neural Bases of Motivated Reasoning: An
fMRI Study of Emotional Constraints on
Partisan Political Judgment in the 2004
U.S. Presidential Election
Individualism, Conservatism, And Radicalism
as Criteria for Processing Political Beliefs: A
Parametric fMRI Study
Neurocognitive correlates of liberalism and
conservatism
A Step into the Anarchist’s Mind: Examining
Political Attitudes And Ideology Through
Event-Related Brain Potentials
Biology, Politics, and the Emerging Science
of Human Nature
Brain Imaging and Political Behavior: A
Survey
Is Political Cognition Like Riding a Bicycle?
How Cognitive Neuroscience Can Inform
Research on Political Thinking
Emotions in Politics
Automatic
processing
Partisanship Interest
fMRI
fMRI
fMRI
Face Judgment
fMRI
Attitude
Partisanship - Party
ID
Face Judgment
Automatic
processing
Partisanship Motivated
Reasoning
fMRI
fMRI
fMRI
fMRI
fMRI
fMRI
fMRI
Attitude
Partisanship - Party
ID
EEG
EEG
Attitude
Review
Review
Commentary
Commentary
Commentary
Lieberman
et al.
2003
Marcus
2000
Marcus et
al.
1998
Linking Neuroscience to Political Intolerance
and Political Judgment
Review
McDermott
2009
The Case for Increasing Dialogue between
Political Science and Neuroscience
Review
Schreiber
2004
Political Cognition as Social Cognition: Are
We All Political Sophisticates?
Review
Review
Review
Commentary
Commentary
Commentary
Commentary
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Spezio &
Adolphs
2007
Theodoridis
& Nelson
2012
Tingley
2006
Emotional Processing and Political Judgment:
Toward Integrating Political Psychology and
Neuroeconomics
Of BOLD Claims and Excessive Fears: A Call
for Caution and Patience Regarding Political
Neuroscience
Neurological Imaging as Evidence in Political
Science: A Review, Critique, and Guiding
Science
Commentary
Review
Commentary
Review
Commentary
Review
Seven of these 27 studies met the inclusion and exclusion criteria applied in the
Bartra et al. meta-analysis (Table 2). Of the remaining 20 studies, ten were reviews and
commentaries on neuropolitics as a field. Of the remaining ten studies reporting primary
results, four used fMRI, two used structural MRI, two used EEG, one used genetics and
one used participants' physiological response to study various political behaviors.
Interestingly, the structural MRI, EEG and genetics studies reported correlates of
subjects' political characteristics rather than studying online decision behavior. Thus,
even though 7/11 fMRI studies passed the Bartra criteria and were therefore judged to be
using a neuroeconomic framework in the study of political choice, in the broader scope of
all neuropolitics studies reporting primary results, this ratio drops to 7/17.
The seven studies that passed our criteria reported data from a total of 187
subjects. Three of these seven studies investigated face judgment, while the remaining
four studied motivated reasoning, political interest, attitude change in response to
advertising and automatic processing of political preference. Across these seven studies,
reporting either a binary contrast or a continuous parametric analysis in a total of 13
tasks, four reported regions linked to behavioral measures of positive SV (for example,
positively rating a politician) and four reported regions related to behavioral measures of
negative SV (for example, negatively rating a politician) (Table 2).
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Within these seven neuropolitics studies, the whole-brain analysis of above
chance clustering of foci across all studies reporting positive SV foci and all studies
reporting negative SV foci from the neuropolitics studies did not yield any significant
overlap across either the positive or negative SV group. The maximum overlap at a single
location (5mm radius around a reported coordinate) was 75% (three out of four studies)
reporting correlates of positive SV and 50% (two out of four studies) reporting correlates
of negative SV. These results are not different than what would be expected by chance,
although due to the small sample-size only a 4/4 match would have sufficient power to
exceed chance. Figure 1 shows all foci of activation related to SV in political contexts
overlaid on the ROIs identified in the Bartra et al. review.
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Table 2: Neuropolitics studies reporting BOLD signal in relation to behavioral
measures of positive or negative subjective value.
SV
Vale nce
Positive
Sample
Topic
Task
Partisanship
Agree vs. disagree with political
and Political
opinions in interested vs.
Inte rest
uninterested subjects
Automatic
Post-hoc ratings of politicians that
Proce ssing
are shown while subject is
of Face s
engaged in a distractor task
Year
Author
Size
Country
Economic SV RO I
al.
Partisan
USA
Putamen
Non-
German
Medial T emporal
Partisan
y
Lobe, Caudate
Non-
USA,
28
Partisans
Japan
None
40
Partisans
USA
None
Partisans
USA
Insula
25
Tusche
2013
Face
et al.
20
Rule et
Judgment
Voting for a political candidate
Political
Changed preference for politician
Attitude
after Positive Political Ad
2010
al.
Subjects
Non-
Gozzi et
2010
O verlaps with
Kato et
2009
al.
Democrats and Republicans
Face
viewing candidate from opposing
Judgment
party versus one from their own
Kaplan
2007
et al.
20
Insula, Dorsal
Ne gative
Face
Not voting for a political
Judgment
candidate
Partisanship
Viewing information threatening
- Motivated
to a political candidate from
Re asoning
subject's own party versus neutral
Political
Changed preference for politician
Attitude
after Negative Political Ad
Non-
Spezio
2009
et al.
24
Anterior Cingulate,
Partisans
USA
T halamus
Partisans
USA
None
Partisans
USA
None
Westen
2006
et al.
30
Kato et
2009
al.
40
Relatively few neuropolitics studies have used designs that can be compared to fMRI
research on decision-making or value in economic contexts. Where comparison was
possible, few foci from the political studies fell within areas that Bartra et al. previously
identified as commonly reflecting either positive or negative SV in economic contexts.
K R A S T E V | 21
Of all the foci reported (130 coordinates in total), only five passed the masking stage of
our ROI analysis, i.e. fell within the brain regions consistently related to SV in rewardbased decision- making paradigms.
The foci associated with positive SV are shown in Figure 1, and were reported by
Gozzi et al. (2010) and Tusche et al. (2013). Gozzi and colleagues (2010) used a task that
measured agreement with political opinions in groups of subjects varying in level of
political interest. Agreement with political opinions in politically interested versus
uninterested subjects was considered a measure of positive SV, and was related to
activation in the putamen. In Tusche et al., (2013), subjects were shown photographs of
familiar politicians while engaged in a distractor task. Viewing politicians previously
rated more positively was related to activation in the medial temporal lobe and caudate.
The foci associated with negative SV are shown in Figure 1, reported in studies
by Kaplan et al. (2007) and Spezio et al. (2009). Kaplan and colleagues (2007) studied
partisans looking at photographs of politicians of an opposing party versus their own
party. Spezio et al., 2009 asked subjects to vote for unfamiliar politicians based on head
and shoulders photographs. Both studies found significant BOLD activation in the insula
that was related to viewing a political candidate that the subject disliked. The coordinates
in these two studies did not overlap, however. Collapsing the data across hemispheres
yielded two more foci associated with negative SV falling within the Bartra ROI, in the
dorsal anterior cingulate and thalamus, both from the Spezio et al. (2009) study. All the
foci overlapping with Bartra et al. (2013) ROI’s for positive and negative SV are shown
in Table 2.
K R A S T E V | 22
Figure 1: Foci derived from neuropolitics studies mapped onto ROI’s derived from
Bartra et al. (2013). A: Positive SV Overlap in Striatum and MTL; B: Negative SV
overlap in Insula, Dorsal, Anterior Cingulate and Thalamus
K R A S T E V | 23
DISCUSSION
A systematic review of the cognitive neuroscience literature studying political
behavior found that a minority of studies address political decision-making online in a
way comparable to past neuroeconomics research. Ten of the seventeen studies we found
aimed to link relatively static political characteristics such as partisanship to neural
variables measured with fMRI, structural MRI, EEG or genetics. The remaining seven,
all of which used task-based fMRI to study political decision-making, were the focus of
our meta-analysis, as they provided evidence of the neural correlates of value-based
political judgments.
Neuroscience research on value-based
decision-making now constitutes a
relatively large body of work, yielding detailed models of value-based decision-making
(Fellows, 2004; Rangel et al., 2008; Kable & Glimcher, 2009; Padoa-Schioppa, 2011,
amongst others). This work has found consistent activation of several brain regions
related to subjective value in a range of economic tasks (reviewed in Bartra et al., 2013),
arguing that these regions support domain-general value-related processes. There is also
some evidence from human lesion studies that damage to the ventromedial frontal region,
including vmPFC and medial OFC, at least, impairs value-based choices across a range
of (largely ‘economic’) contexts (Fellows & Farah, 2007; Camille et al., 2011; HenriBhargava et al., 2012), supporting the claim that this region is necessary for value-based
decisions, broadly defined. Testing the limits of this claim has implications both for
brain-based models of decision-making, and for our understanding of the commonalities
and differences between economic and political choice.
K R A S T E V | 24
Given this, it is surprising that relatively few neuropolitics studies make use of
designs or analytic frameworks analogous to neuroeconomic studies where choice is
studied in a reward-processing context. The handful of studies identified in this review
represents too small a sample for strong interpretations of the quantitative meta-analysis
results. However, even at a qualitative level there were very few consistent foci
associated with SV in political contexts. The tasks which did report foci overlapping with
ROI’s from Bartra et al. (2013) were generally focused on ratings of politicians rather
than more abstract concepts such as political attitudes. However, even within contrasts
taken from these tasks, a relatively small fraction of foci overlapped with previously
identified correlates of SV. While preliminary, this is a challenge to the view that value
processing in the brain is content-general, i.e. that the same circuits are engaged
regardless of the nature of the decision.
Although no significant effects were detectable in the meta-analysis presented
here, qualitative assessment of the few neuropolitics studies that do report foci within the
‘economic’ SV-related ROIs may be useful for hypothesis generation. In the study by
Gozzi et al. (2010), political interest was used to differentiate groups of subjects that
evaluated political statements.
The behavioral measure of positive SV in this study was
the contrast between agreement and disagreement with a statement in high versus low
political interest individuals. Since economic SV experiments often require that a subject
is explicitly motivated to receive a reward of the type offered during the study (e.g. food
study subjects are told to fast before the session, and receive a chosen food item at the
end of the experiment), it seems possible that a certain amount of motivation, such as that
measured by political interest, is important for political value processes to engage neural
K R A S T E V | 25
circuits common to those implicated in economic value. In the Tusche et al. (2013) study,
the behavioral measure of positive SV was a positive post-hoc rating of photos of
politicians viewed by subjects while they were engaged in a distractor task. In this case,
results suggest that automatic processing of political preference may elicit increased
activity in areas of the brain associated with economic value assignment.
In the negative group, the two studies by Kaplan et al. (2007) and Spezio et al.
(2009) both yielded overlapping foci from tasks which measured BOLD correlates of
negative SV. In the Kaplan study, SV was measured by asking participants to look at
photographs of politicians from an opposing party. In the Spezio study, subjects were
asked to vote for unfamiliar politicians based on a photograph of their face. In both cases,
activity in the insula was correlated with viewing the photograph of a disliked politician
(as measured by party preference or vote, depending on the study). Given the insula’s
previous association with emotional processing (Wright et al., 2004; Phan et al., 2002)
and the fact that subjects are viewing a disliked politician, it is possible that these tasks
elicit a negative emotional response. In the case of the Kaplan study, the response may be
an instance of pre-established distaste for both the politician (assuming he/she is familiar)
and the associated party.
These preliminary findings raise the possibility that the neural substrates of SV
identified in economic choice paradigms may not generalize to the types of political
choices studied in neuropolitics work to date. One potential difference between political
and economic choice is the extent to which decision-makers rely on heuristics. Although
economic frameworks encompass choices made based on simplifying heuristics, such as
brand loyalty, neuroeconomic studies have largely steered away from what political
K R A S T E V | 26
science would term heuristic choices (e.g. Hauser, 2011), instead focusing on paradigms
where choices more clearly hinge on deliberations about subjective value. While such a
focus obviously has merits, the study of heuristic decision-making has continued to grow
in prominence in political science and behavioral economics, as social science comes to
better understand how humans grapple with informational complexity. Indeed, as
profound ignorance of many facets of political life has become commonplace in the
general public (Delli Carpini & Keeter, 1995), heuristics may serve as something of the
last refuge for democratic decision-making and governmental accountability (Lupia,
1994; Popkin, 1991). Based on the prevalence of heuristics in political choice and the
possible differences between the neural substrates of economic and political choice, one
conclusion we can draw is that comprehensive decision neuroscience must consider
choices beyond the economic realm, with more work needed to address choices where
deliberative economic models of value assignment fall short.
Another possibility is that there is a misalignment in the frameworks used to study
these
economic
neuropolitics
and
study
political
designs
choices.
that
allow
Stronger
ready
conclusions
comparison
would
with
necessitate
the
existing
neuroeconomics literature. We propose that such studies would be of high interest to both
neuropolitics and neuroeconomics research, regardless of their outcomes. Defining the
extent to which current models of subjective value apply or do not apply in political
choice will enrich our understanding of the brain basis of both of these important aspects
of human behavior.
K R A S T E V | 27
CHAPTER 3: Are decision behaviors similar in economic and political choice?
BACKGROUND
In Chapter 1, we examined the extent to which the methods of cognitive
neuroscience have been used in the study of political behavior. Neuropolitics is still a
very young field. Some of the work to date does address political decision-making, but
varies in how closely it follows frameworks emerging from neuroeconomics research.
Interestingly, those fMRI studies that could be directly compared showed little overlap in
the subjective value-related activations. This raises the possibility that political choice is
inherently different from the ‘economic’ choices that have been the main focus of
neuroeconomics to date.
There are potentially many differences between political and economic choices.
For one, as previously discussed, the higher informational complexity inherent to most
political choices may push political decision makers to use cognitive shortcuts (Schaffner
& Strebb, 2002). An obvious example is the partisanship heuristic, which allows choices
to be simplified to the point of following pre-established rules (e.g. vote for the candidate
representing ‘my’ party). Although some economic choices can also be simplified in this
way (e.g. brand loyalty), existing neuroeconomics research has typically avoided
paradigms where decisions can be made by the use of heuristics, focusing instead on
deliberative choice. Overall, there is little evidence one way or the other that addresses
whether political and economic decision-making relies on common processes and neural
substrates.
K R A S T E V | 28
We hypothesize that existing neuroeconomics models can be usefully applied to
understand political choice. A first step in testing this hypothesis is to examine whether
the simplifying heuristics that are an important feature of political choice can be
accommodated by existing behavioral models of decision-making.
Binary choice preference tasks are widely used paradigms in decision psychology
and neuroeconomics. They are simple and ecologically valid, and can provide data on the
relationship between prior item ratings and choice, as well as decision time. Further
insight into the decision process can be gained by measuring eye movements, an
established proxy for information gathering during deliberation in these tasks. Formal
computational models of such tasks exist that reproduce many of the observed behavioral
phenomena, providing an explicit explanatory framework that has been linked, at least
provisionally, to underlying brain networks (Lim et al., 2011).
Past work on evidence gathering during economic choice has found consistent
patterns in the choice behavior and eye movements of subjects during value-based binary
choice tasks (Milosavljevic et al., 2010). Firstly, past studies of binary economic choice
have shown that the probability of choosing a given option increases as the difference
between the value rating (made a priori) of that option and its alternative increases.
Secondly, past studies show that reaction time (RT) decreases as the difference in value
rating between the two alternatives increases. Thus, a priori value ratings are a good
predictor of both choice probability and RT during binary choice. These findings have
been interpreted by some as support for the idea that deliberative decisions are driven by
an evidence-gathering process in the brain (Krajbich et al., 2010).
K R A S T E V | 29
Visual fixation patterns from similar studies also support this idea. Visual
fixations during each trial have been studied with respect to the time of stimulus
presentation and choice: binned into first (the initial eye movement when the stimuli are
presented), middle (all subsequent shifts in direction of gaze) and last (the fixation ongoing at the time the choice is registered). It has been shown that during economic
choice, final fixations are significantly shorter than middle fixations. This has been
interpreted as support for evidence gathering to a decision threshold, which, when
reached, interrupts the final fixation. Furthermore, the item fixated during the last fixation
is far more likely to be chosen than its alternative. One explanation for this pattern is that
the value of the unattended (i.e. non-fixated) item is discounted. Thus, studies so far
support the idea that deliberative choice involves an evidence gathering process that can
be indexed by visual fixations.
Evidence Gathering during Binary Choice
Information gathering has been studied extensively in the context of perceptual
decision-making, yielding several models that attempt to bridge the gap between stimulus
and response (Gold & Shadlen, 2007). These models, which have largely focused on
binary decisions, consider choice as a three step process:
gathering evidence for each
alternative; integrating random noise into the evidence; and making a choice once a
sufficient level of evidence (signal:noise) has been reached that favors one alternative
over the other (Bogacz et al., 2006).
The Drift Diffusion Model (DDM) (Ratcliff, 1978), derived from sequential
analysis and signal detection theory, is perhaps the most widely-used model in studies of
perceptual processing. Human and animal studies (Gold & Shadlen, 2007) have shown
K R A S T E V | 30
that the DDM provides accurate predictions of behavioral and neural metrics during
binary perceptual choice (Milosvljevic et al., 2010).
Attentional Drift Diffusion Model
In an effort to extend this literature beyond perceptual choice to value-based
decision-making, a derivative of the DDM was developed which uses the same
evidence gathering principles, with eye fixations as a proxy for visual attention
(Krajbich et al., 2010). This model, called the attentional Drift Diffusion Model
(aDDM, see Figure 2), updates a decision value toward one of two decision barriers
(representing the two alternatives being weighed) but does so faster toward the
alternative that is currently being visually fixated (Figure 1). The attentional Drift
Diffusion Model has been shown to predict RT, choice probability and visual fixation
patterns for food stimuli (photos), within certain decision contexts such as high and low
time-pressure (Milosavljevic et al., 2010), and purchasing decisions (Krajbich et al.,
2012) and has been adapted to three-alternative choices (Krajbich & Rangel, 2011).
Furthermore, an fMRI study using this paradigm (food stimuli) has shown that rating
differences between two competing items are correlated with levels of activity in the
ventromedial prefrontal cortex and striatum (Lim et al., 2011). Finally, a recent study of
moral decision-making showed that the aDDM can fit decision behavior beyond strictly
‘economic’ contexts (Parnamets et al., 2014).
Figure 1: attentional Drift Diffusion Model - Equation. Value at
time t is equal to the value at t-1 plus d (the drift rate) times the
difference between the rating previously given to the attended option
(in this case the left) minus theta (the discount rate) times the
unattended option (in this case the right) plus epsilon (Gaussian noise).
K R A S T E V | 31
Figure 2: Attentional Drift Diffusion Model – Random Walk.
The light and dark grey sections represent times during which the
subject is looking left and right. The resulting drift toward the
attended item is a result of the discounting of the unattended item
(Krajbich et al., 2010).
The aDDM’s solid grounding in perceptual choice work, its use in the study of an
increasingly wide variety of economic choices, and the existing evidence relating its
parameters to brain processes make it a good starting point for studying political choice
in a way that can be compared to existing neuroeconomic work.
Specific Aims
The study presented here takes the first steps toward using the methods of
cognitive neuroscience to directly compare evidence gathering during economic and
political choice, beginning with a behavioral task paired with eye-tracking. Since past
work on evidence gathering during economic choice has yielded specific predictable
choice behavior and eye-movement patterns, we asked whether these same patterns
would be apparent during political choice. In particular, we aimed to
K R A S T E V | 32
1. Determine whether evidence gathering during political choice yields the
same behavior and eye movement patterns as previously reported for
economic choice.
2. Explore the effects of partisanship on evidence gathering as indexed by
choice behavior and eye movements.
To address these aims, we adapted the procedure developed in binary foodchoice research by Krajbich and colleagues (2010) and applied it to voting decisions
between photographs of political candidates. Past research has shown that votes based
solely on appearance can predict election outcomes, suggesting that this political choice
laboratory paradigm may have some real world validity (Todorov et al., 2005).
We predicted that in the absence of any information except candidate
appearance, these political choices would engage very similar mechanisms to economic
choices, resulting in behavioral and eye-tracking patterns similar to those reported in
the existing food-choice studies. In a second step, we provided party information about
some of the candidates, and compared partisan and non-partisan participants to look for
behavioral and eye-tracking evidence of the use of the partisanship heuristic. We
predicted that Liberal Party information would completely dominate the decisionmaking process of Liberal partisans, causing their choice behavior and eye movements
to deviate from the patterns seen in prior economic paradigms in trials where they are
given such information. Finally, we explored whether the aDDM captures the eye
tracking behaviors observed in this political paradigm, in the presence and absence of
party information. This work is a starting point in determining whether existing brainbased models of value-based decision- making can be applied to political choices.
K R A S T E V | 33
Predictions
When no party information is presented, we predict that partisan and non-partisan
participants will exhibit comparable choice behaviors, and that these will be similar to
what has previously been observed during economic choice: i.e. probability of choosing
an option will increase and RT will decrease as value rating difference increases. When
party information is given, we predict that these behavioural patterns will be unaffected
in non-partisans, but that partisans will become insensitive to the value ratings they have
previously provided, both in their choices and RT. Furthermore, we predict that partisans
will exhibit faster RTs in this condition compared to non-partisans, because they will use
a “choose my party” heuristic.
When no party information is presented, we predict that both groups will base
their decision on an evidence gathering process that has previously been observed in
economic choice studies, exhibiting the same eye movement patterns described above –
shorter final versus middle fixations and a higher likelihood of choosing the last seen
item. However, when party information is introduced, we predict that partisans will
simply explore the screen until the Liberal candidate is found. We therefore expect to see
fewer middle fixations. The prediction for final fixations is less clear:
both a heuristic
and deliberative strategy would yield final fixations shorter than middle fixations,
although possibly due different mechanisms (i.e. in the former, by triggering a stimulusrule mapping such as “choose red frame”, in the latter when the decision-value barrier for
“I prefer this option” is crossed). We expect partisans in the party information condition
to show no bias toward the last attended item. That is, they will choose the Liberal
candidate whether it is the last one they view or not. Any subtle attention-mediated bias
otherwise detectable in this task will be entirely superseded by party information.
K R A S T E V | 34
METHODS
Subjects
Subjects were recruited from databases of healthy volunteers maintained by the McGill
Centre for the Study of Democratic Citizenship and the McGill Cognitive Neuroscience
Research Registry, originally recruited either through web surveys or community
advertisement. All subjects were screened using a standard medical questionnaire and
those with a history of significant neurological or mental health issues and/or
psychoactive medication use were excluded from the experiment. They were given a
political attitude and knowledge questionnaire (see Fig. 3) and assigned to one of two
groups based on their answers to the questions “Which political party do you ally
yourself most strongly with?” and “How strongly?” Subjects who indicated that they
were “fairly strong” or “very strong” Liberals were assigned to the (Liberal) partisan
group and subjects who indicated either “don’t know” (7/12 subjects) or “none of
these” to the first question were assigned to the non-partisan (i.e. non-Liberal-partisan)
group. Subjects were also asked about their level of political interest. They provided
written informed consent and received 15$/hour to compensate them for their time and
inconvenience. The research protocol was approved by the MNI Research Ethics Board.
Materials
The stimulus set consisted of 214 head and shoulders photographs of real-life
gubernatorial candidates from American elections spanning 1995-2004. The black and
white photographs were converted to bitmap format and re-scaled to a width of 200
pixels. They were presented on a 17 inch Viewsonic color monitor set to a resolution of
640 x 480 pixels and located at a fixed distance of 59 cm from the subject’s eyes.
K R A S T E V | 35
Stimuli thus subtended 12.5 visual degrees. Colored frames were added to the
photographs, matched for contrast with the background .
Figure 3: Political Questionnaire used to establish subjects’ political interest
and party affiliation. Liberal partisans were defined as those chose “Liberal” in
Q2A and “Fairly Strongly” or “Very Strongly” in Q2B. Non-partisans were those
who chose “None of These” or “Don’t Know” in Q2A.
Eye-Tracking
Visual fixations were recorded using an EyeLink 1000 desktop-mounted eye
tracker to track the dominant eye of each subject. The eye-tracker was calibrated to an
accuracy of +/- 1.5 visual degrees.
Procedure
Subjects were given on-screen instructions and told that they will make choices
regarding a series of politicians. The experiment was then divided into two phases: the
rating phase and the choice phase.
K R A S T E V | 36
During the rating phase, subjects were asked to rate all 214 stimuli, presented in
a random order, on: “How much would you want to vote for this person in a real life
federal election?” (scale -3 to +3). Each photograph was presented in the middle of the
screen, without any response time-limit. Participants responded by mouse-clicking on
the rating scale (Fig. 4).
Figure 4: Phase 1 - Rating. Photographs
were rated one-by-one, without any party
information. (Actual photograph not
shown to respect copyright.).
Trials for the next phase were then generated using a subset of the initial
photographs according to the following rules: only photographs rated 0 to 3 were
included; 68 (34 unknown - unknown and 34 unknown – Liberal) pairs with an absolute
difference of 0 were generated; 68 (34 unknown - unknown and 34 unknown – Liberal)
with an absolute difference of 1 were generated; 68 (34 unknown - unknown and 34
unknown – Liberal) pairs with an absolute difference of 2 were generated (see Figure
5). unknown party was indicated by a purple frame and Liberal party was indicated by a
red frame around the candidate photographs. In order to assure that choices were not
K R A S T E V | 37
biased by the salience of the frame, the frame colors were chosen to have equal contrast
against the background.
Figure 5: Choice stimuli selection Positively
rated photographs from the rating phase were
randomly assigned a purple or red frame. 198
pairs were then created, so that there would be 33
pairs with a rating difference of 0, 1, 2 in the
unknown-unknown Condition and 33 pairs with a
rating difference of 0, 1, 2 in the unknownLiberal condition.
During the binary choice phase, the 198 pairs generated at the end of the rating
phase were shown in random order. Before the trials, subjects were asked “Which
politician would you vote for in a real-life federal election?” (See Figure 6). The eyetracker was used during this phase in order to record visual fixations. Prior to the
presentation of each binary choice stimulus pair, the subject was required to maintain
fixation on a cross at the center of the screen for 2 seconds. Subjects were then
presented with the two stimuli and asked to indicate their choice by pressing one of two
K R A S T E V | 38
keys, without a time limit. Following completion of this phase, subjects were debriefed
– they had the chance to ask questions about the general purpose of the study and were
asked about their partisanship and level of political interest to confirm the intake
screening.
Figure 6: Choice phase. The pairs
generated from Phase 1 were shown in Phase
2, and subjects were asked to choose
between them, based on how likely they
would be to vote for them in a real-life
federal election.
Data Analysis
Initial data collection was accomplished on E-Prime 2.0 and EyeLink, yielding a
time-series of eye fixation coordinates. Raw data were then processed with a Matlab
script that identified whether each fixation location fell within an ROI (i.e. within the
bounds of one of the two stimuli). An uninterrupted sequence of time-points during
which fixations fell within the bounds of one stimulus was counted as a single
‘fixation’ on that stimulus, following the original methodology (Krajbich et al., 2010)
(terminology that we note differs from the convention in the oculomotor literature).
K R A S T E V | 39
The choice and eye-tracking data were analyzed using mixed ANOVAs, followed by
Simple Effects tests and post-hoc Tukey’s HSD. Error bars on the graphs presented
below indicate the standard error of the mean.
RESULTS
Demographic Results
Forty-eight people were screened with the political questionnaire, and the 40 who
met criteria as either non-partisans or Liberal partisans were included in the study. The
study design required a minimum number of positively rated items in the rating phase in
order to generate the binary choice phase. Twelve subjects were excluded after the rating
task for not reaching this threshold – this is a relatively high number compared to past
studies on economic choice, likely related to the fact that previous experiments used
images of primary rewards (i.e. food), while the photographs of unknown (actual)
political candidates used here were not especially appealing in the absence of other
information. A further 4 subjects were excluded because the eye-tracker could not
consistently follow their gaze even after several recalibrations (likely due to eye color,
glasses and eye size). Full data are available for the remaining 24 subjects. Demographic
and political interest data for these subjects is shown in Table 1. The Liberal partisan and
non-partisan groups did not differ significantly in age, sex, education or political interest
(t-tests, all p > 0.05).
N
Females
Age
Education (y) Political Interest (0-2)
NonPartisans
12
5
48.8 (9.8)
15.9 (1.6)
1.5 (0.5)
Partisans
12
7
45.1 (11.3)
15.5 (2.5)
1.3 (0.6)
Table 1: Demographic information and level of political interest of the sample, split by
extent of (Liberal) partisanship, expressed as counts or mean (SD).
K R A S T E V | 40
Choice Task Results
Choice
We first examined basic performance on the choice task. There was no significant
bias toward choosing the option presented on the left or right in either group: a mean of
50% (SD 5%) and 52% (SD 5%) of choices were for the left option in partisans and nonpartisans, respectively.
As expected, a priori value ratings predicted choices. In the no party information
condition, a mixed ANOVA yielded a significant main effect of rating on choice (F(4,
88) = 55.05, p < 0.01) but no significant main effect of party on choice nor a significant
party-rating interaction (Figure 7a). In the party information condition, a mixed ANOVA
once again showed a significant main effect of rating on choice (F(4, 88) = 15.59, p <
0.01) as well as a significant interaction between partisanship and rating (F(4, 88) = 7.14,
p < 0.01) (Figure 7b). Note that in this condition, new information is available that was
not used to make the initial rating, causing us to predict that partisans would no longer be
affected by rating. Indeed, a simple effects test showed a significant effect of rating on
choice in non-partisans (F(4, 88) = 21.45, p < 0.01), but not in partisans. However, there
is evidence that the partisans were not exclusively following a simple ‘pick my party’
heuristic: when party information was present, partisans chose the Liberal candidate only
87% (SD 12%) of the time.
RT
RT differed significantly between groups (mean (SD): partisans 1897 (795) ms;
non-partisans 3530 (2350) ms). We tested whether RT related to decision ‘difficulty’ as
K R A S T E V | 41
operationalized by rating difference between candidate pairs. In the no party information
condition, a mixed ANOVA yielded significant main effect of rating on RT (F(2, 44) =
4.43, p < 0.05) and a trend towards an effect of group on RT (F(1, 22) = 3.04, p = 0.09),
but no interaction between group and rating (Figure 7c). In the party information
condition, a mixed ANOVA also yielded a significant main effect of rating on RT (F(2,
44) = 3.77, p < 0.05) and of group on RT (F(1, 22) = 5.02, p < 0.05), but no significant
interaction (Figure 7d). Tukey’s HSD revealed a significant difference between ratings 1
and 2 in non-partisans (p < 0.05), but in none of the partisan rating contrasts, suggesting
that in this condition non-partisans are sensitive and partisans are insensitive to ratings.
To follow up the unexpected finding that partisans trended towards faster choices than
non-partisans, we examined the pattern of decision time across trials. A time-series
reveals that the difference is apparent immediately, and persists throughout (Figure 8).
K R A S T E V | 42
Figure 7: Choice behavior results showing choice probability and RT across groups and
conditions.
K R A S T E V | 43
Reaction Time Across Trials
12000
8000
6000
4000
2000
0
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97
101
105
109
113
117
121
125
129
133
137
141
145
149
153
157
161
165
169
173
177
181
185
189
193
197
Reaction Time (ms)
10000
Trial Number
Figure 8: Reaction times across all trials, showing that reaction time differences are present from the beginning of each session
and thus suggesting that they are likely not an artifact of the task design.
K R A S T E V | 44
Eye Movement Results
Next, we examined fixation patterns. As expected, we found no significant bias toward
the right or left (mean (SD): partisans 8(79) ms; non-partisans 69 (124) ms). We also found that a
majority of final fixations (mean (SD): partisans 75% (12%); non-partisans 74% (10%)) were
toward the item that was ultimately chosen, as in prior work.
Final vs Middle Fixation Length
We then tested the prediction that final fixations would be shorter than middle fixations.
In the no party information condition, a mixed ANOVA yielded a main effect of fixation type on
fixation duration (F(1, 22) = 15.89, p < 0.01), and a trend toward an effect of party fixation
duration (F(1, 22) = 3.41, p = 0.08), but no interaction between group and fixation type (Figure
9). Tukey’s HSD revealed that final fixations were significantly shorter than middle fixations for
both non-partisans (p < 0.05) and partisans (p = 0.05). Although fixation duration did not differ
significantly between groups, a one-tailed t-test revealed that the number of fixations per trial
was significantly lower in partisans (mean (SD): partisans: 3.0 (0.8); non-partisans: 3.7 (1.3); p <
0.01).
In the party information condition, a mixed ANOVA yielded a significant main effect of
fixation type on fixation duration (F(1, 22) = 19.96, p < 0.01), and a trend toward an effect of
party on fixation duration (F(1, 22) = 3.64, p = 0.07), but no interaction between group and
fixation type (Figure 9). Tukey’s HSD revealed that final fixations were significantly shorter
than middle fixations for both partisans (p < 0.05) and non-partisans (p < 0.01). Although
fixation duration did not differ significantly between groups, a one-tailed t-test revealed that the
number of fixations was significantly lower in partisans (p < 0.01; mean (SD): partisans: 2.4
(0.9); non-partisans: 3.5 (1.2)).
K R A S T E V | 45
Probability of Choosing Last Seen Item
We next tested whether there was a choice probability advantage for the last seen item. In
the no party information condition, a mixed ANOVA yielded a significant main effect of rating
on choice (F(4, 88) = 32.00, p < 0.01) but no significant effect of partisanship or interaction
between partisanship and rating (Figure 10a). Importantly, at rating difference 0, the last attended
item had a 74% (SD 12%) chance of being chosen in both groups, which differs significantly
from what we would expect if there was no advantage for the last attended item (50%),
confirming the pattern seen in studies of economic choice.
In the party information condition, due to the assumed rating boost provided by
identifying a candidate as Liberal to a Liberal partisan, the analysis considered instances where
the last attended item was the Liberal candidate separately from instances where it was the
unknown-party candidate. When the unknown-party candidate was attended to last, a mixed
ANOVA yielded a significant main effect of group on the probability of choosing the last seen
item (F(1, 22) = 10.26, p < 0.01) as well as a significant main effect of rating on choice (F(4, 88)
= 15.41, p <0.01), but no significant interaction between group and rating (Figure 10b). At rating
difference 0, the last attended item was chosen by non-partisans 64% (11%) of the time and by
partisans 25% (9%) of the time, showing the different impact of party information in the two
groups . However, when the last attended unknown candidate was rated 1 or 2 points higher than
the Liberal alternative, partisans chose that candidate 52% (SD: 13%) and 49% (SD: 11%) of the
time, suggesting that party information was being integrated into the information gathering
process, rather than triggering a completely separate heuristic strategy “blind” to information
other than party affiliation.
K R A S T E V | 46
When the Liberal candidate was the last attended, a mixed ANOVA yielded a significant
main effect of group on the probability of choosing the last seen item (F(1, 22) = 17.71, p < 0.01)
as well as of rating on choice (F(4, 88) = 12.31, p < 0.01). Partisans were far more likely to
choose the Liberal candidate (mean (SD) at rating difference 0: 96% (7%) for partisans; 85%
(9%) for non-partisans). There was also a significant interaction between partisanship and rating
difference (F(4, 88) = 6.59, p < 0.01) (Figure 10c). Simple effects tests showed a significant
difference between groups at rating differences -2 (F(1, 87) = 31.36, p < 0.01) and -1 (F(1, 87) =
8.53, p < 0.01). Rating also had a significant effect on the probability of choosing the last seen
item in non-partisans (F(4, 88) = 18.41, p < 0.01) but had no significant effect in partisans.
K R A S T E V | 47
Fixation Length
First Fixation
1200
Middle Fixation
Final Fixation
Fixation Legnth (ms)
1000
800
600
400
200
0
No Party Info
Party Info
NON PARTISAN
No Party Info
Party Info
PARTISAN
Figure 9: First, Middle and Final fixation lengths are compared across groups and conditions,
showing significantly shorter final versus middle fixations across all groups and conditions.
K R A S T E V | 48
Figure 10: Choice probability curves for the last attended item across a) no party
information trials, b) party information trials where the last attended is the unknown
candidate and c) party information trials where the last item attended is the Liberal
candidate.
K R A S T E V | 49
Simulation
The aDDM model was initialized with published parameters that capture behavior in food
choice versions of this task (drift rate of 0.0002; discount rate of 0.3; Gaussian noise with sigma
0.02; barriers of +/-1; initial decision value of 0), and the parameters were then systematically
varied to explore which most closely mimicked the observed group and condition differences. A
time-series simulation was run with 1000 cycles and a rating scale of -2 to 2, varying parameters
at 10% increments.
We hypothesized that one of drift rate, discount rate, noise or barrier change would
explain group differences in the No Party information trials, as these parameters affect both
choice alternatives symmetrically. Similarly, we hypothesized that varying the initial decision
value would explain condition differences in the Liberal partisans, as this is the only parameter
which, as in this condition, can affect the two alternatives asymmetrically (i.e. favoring the one
to which party information is added). Further, we expected the initial value would have changed,
had the a priori ratings been made with party information provided.
Parameter Fitting
The default parameter values were used in the first simulation (Fig. 11a), yielding a
choice probability curve that was identical to the one observed in the no party information
condition (all simulated data points fell within the 95% confidence interval of the observed data
(+/- 0.083)). However, while the RT simulation yielded a curve that was similar in shape to the
observed data, it lay between the observed non-partisan and partisan curves: The simulated RT’s
were slower than those seen in partisans and faster than those seen in non-partisans. We therefore
asked which parameter change would yield a curve falling within the 95% confidence interval of
the RT data we observed.
K R A S T E V | 50
The only parameter change that yielded data consistent with the observed RT curve in the
partisan no party information condition was lowering the decision threshold from 1 to 0.9 (see
Figure 11b). A simple downward transposition fit the simulation curve to the data. In contrast,
increasing the drift rate affected the slope of the curve in a way that deviated from the observed
data (Figure 11c). Similarly, increasing the Gaussian noise sigma value affected both the RT and
choice probability curves, making both much flatter than observed (Figure 11d). Finally,
changing the starting decision value to 0.7 flattened both the choice probability curve and the RT
curve, and shifter the latter downward (Figure 11e). These changes approximated the data in the
partisans (party information) condition, with 4/5 choice probability points and 5/5 RT points
falling within the 95% confidence interval of the data. Thus, new relevant information provided
to partisans can be modelled by a shift in the starting decision value of the aDDM. This finding
fits the common sense expectation; it is likely that value ratings for individual candidates would
be higher in Liberal partisans if party information had been provided when the ratings were
made.
The preliminary model fitting suggests that group differences can be captured by changes
in the decision barrier in the aDDM. While further experimental and modelling work is needed to
make stronger interpretations of this finding, it raises the possibility that partisans and nonpartisans differ on the level of evidence required to make a political decision.
K R A S T E V | 51
K R A S T E V | 52
Figure 11: Matlab simulation results using 1000 runs of the aDDM
equation with a) default parameters, b) lowered decision barrier, c)
increased drift rate, d) increased noise and e) a shifted starting decision
value
K R A S T E V | 53
DISCUSSION
The first aim of this study was to determine to what extent political choice conforms to
behavioral patterns previously observed in economic choice, when studied in a similar paradigm.
The condition in which no party information was provided is the closest to existing economic
work: participants had only the appearance of each candidate to guide their voting choice, and
hence engaged in what was presumably on online computation of value. We found that choice
probability and RT patterns in non-partisans corresponded to what has been reported in past
studies with non-political stimuli, with the probability of choosing a particular candidate
increasing as rating difference increased, and RT decreasing as rating difference increased.
However, the patterns of behavior were different in the Liberal partisan group. Unexpectedly,
there was a trend for differences in partisan and non-partisan choice behavior even in the ‘no
party information’ condition.
Although partisans were as likely as non-partisans to choose the candidate they had rated
higher, their RT’s tended to be faster and they made significantly fewer fixations even in the
absence of party information. Since this condition was informationally equivalent for the two
groups, our findings indicate that either partisans in general, or Liberal partisans specifically,
tend to gather less information (assuming that RT and fixations are indicative of evidencegathering) than non-partisans before making a choice. Preliminary fitting of aDDM parameters
suggests that this might reflect a lower decision threshold amongst partisans. Past work lends
some support to this idea (Carney et al., 2008), characterizing Liberals as being more openminded and novelty-seeking (as assessed by standard personality measures) compared to
conservatives, but to our knowledge, there has been no work comparing them to non-partisans,
nor showing that they exhibit faster RT’s. Further studies are needed to determine whether this
K R A S T E V | 54
apparent RT difference can be replicated, to test whether it holds for partisans of other parties,
whether it is present in non-political decision tasks, and whether it reflects a general personality
feature or is specific to political choice.
The task and analytic approach developed here would be suited to addressing these
questions, with the inclusion of an economic choice condition, such as between food stimuli as
studied in other work (e.g. Krajbich et al., 2010). A perceptual control condition involving a
sensory rather than value-based decision would address whether the faster choices of partisans
reflect a general lowering of response threshold. . Finally, standard personality questionnaires,
similar to those used by Carney and colleagues (2008) to identify open-mindedness and novelty
seeking in liberals could also address whether partisanship is one facet of a more general
personality style, or specific to political behavior. Finally, longitudinal studies could address the
causal direction between liberal partisanship and the lower decision boundaries that we report in
the present study.
A more trivial potential explanation of the difference in RT between Liberal partisan and
non-partisan groups observed here is that it was an artifact of task design, induced by
interleaving party information and no party information conditions. This seems a less likely
explanation given the observation that RT differences were evident even in the first few trials,
with little change over the task. However, a variation of the same task with conditions varied by
block would more thoroughly test this possibility.
The eye tracking data supported the hypothesis that when only visual appearance was
given as information, both groups employed decision processes analogous with those seen in
economic choice: both groups exhibited significantly shorter final versus middle fixations as well
as an increased likelihood of choosing the last seen option.
K R A S T E V | 55
The choice behavior and eye movement findings in the no party information condition
suggest that evidence drawn from appearance during voting choices is processed in a way similar
to food choices studied in the past (Krajbich et al., 2010). These findings agree with other recent
work showing that the aDDM can be applied to a broader range of tasks (Parnamets et al., 2014;
Krajbich et al., 2012), and support the idea that the explanatory framework proposed for simple
economic choices in this paradigm can be generalized to a variety of choices. Further work is
needed to test the limits of this claim.
As an initial step in exploring these limits, the second aim of this study was to examine
the effects of partisanship on evidence gathering as indexed by choice behavior and eye
movements. The party information condition adds further information, which we hypothesized
would have a different influence on decision-making in Liberal partisans and non-partisans.
Based on past research on the partisanship heuristic in low information elections (Schaffner &
Streb, 2002), we expected that the former group would follow a simplifying heuristic, which
would substantially alter decision behavior, leading to faster and more stereotyped (ie. ‘choose
Liberal’) choices, insensitive to the social information from the faces that we assume guides
decisions in the no party information condition.
The choice probability and RT curves for partisans in the party information condition
indicate that displaying a Liberal candidate during a trial makes partisans less sensitive to the
ratings they previously gave based on a candidate’s appearance. In fact, there was no significant
effect of rating on partisans’ choice probability or RT, suggesting that party information
supersedes information drawn from the appearance of the candidates. However, the eye
movement results tell a more nuanced story.
K R A S T E V | 56
Final fixations were shorter than middle fixations in both groups across conditions. This
result conforms to the predictions made by past studies of economic choice, supporting the idea
that subjects may be engaging in evidence gathering until a decision threshold is reached. This
finding does not allow us to identify whether partisans were using a “choose-my-party” heuristic
or deliberating, as we would expect a barrier to be reached and thus interrupt the final fixation in
both cases. However, we did predict that partisans in the party information condition would
adopt a simpler search approach, quickly shifting their eyes to the red-framed candidate,
resulting in fewer and shorter fixations. Although the difference in fixation duration only trended
towards significance, partisans did indeed show significantly fewer fixations. Strikingly, this
trend was also evident in partisans in the no party information condition.
The last attended item data also support the conclusion that party information did not
engage a heuristic that led to decisions entirely blind to any information except party affiliation.
When the party affiliation of the last attended candidate was unknown and that candidate was
previously rated 1 or 2 units higher than the alternative, partisans chose that candidate roughly
50% of the time. This supports the claim that partisans continue to gather evidence beyond party
information, remaining at least somewhat sensitive to the factors that contributed to their prior
value estimates based on candidate appearance as well.
Although very little work has been done to explore how heuristics interact with evidence
gathering, the findings here raise the possibility that party information does not engage a unique
decision process, but rather integrates with other factors to drive a decision value toward a
barrier. This possibility is supported by the little research done on the topic. In a study on
branding, Philiastides and Ratcliff (2013) used a preference-based decision-making task and
computational modelling to show that brand information and subjective preference are likely
K R A S T E V | 57
integrated into a single source of evidence in the decision-making process.
Further work is
needed to clarify the mechanisms underlying decision-making heuristics, as well as to shed light
on the neural substrates of these processes.
Alternative Decision Models
We have thus far framed this study in the context of past research on economic
decision-making. However, our analysis makes assumptions about the nature of evidence
gathering during choice that remain to be confirmed. Although models that describe integration
of decision value toward one of two decision barriers such as the attentional Drift Diffusion
Model have seen some success in predicting choice behavior (Ratcliff, 1978; Smith & Ratcliff,
2004), eye movements and brain activation patterns during economic choice, there is a larger
set of viable models that describe evidence gathering during 2-Alternative Forced Choice
tasks. Some of these, such as the Ornstein-Uhlenbeck model, are extensions of the formal logic
behind the DDM and present a serial mode of evidence gathering. In the case of the OrnsteinUhlenbeck model, this is achieved by adding a “current accumulation state” term to the basic
DDM equation which makes the current rate of evidence accumulation for each option
dependent on the value accumulated for each option so far. Other models, classified as
“accumulators”, rely on a parallel processing mechanism to gather evidence and are thus
formally quite different from the DDM as they no longer represent a random walk. In one such
popular alternative called the Race Model (Vickers, 1970), evidence is accumulated for both
options in parallel (and is thus presumed to be unaffected by attention). The option which
reaches a predetermined threshold first is the one which is ultimately chosen. In the Mutual
Inhibition Model, on the other hand, evidence gathered in parallel for each alternative serves to
dampen evidence gathering for its alternative (Usher & McClelland, 2001).
K R A S T E V | 58
Although there has been significant support for both diffusion and accumulator type
models in past years, the DDM (and aDDM variation) have provided better fits to the data
from various perceptual and value-based choice tasks (Milosvljevic et al., 2010). Furthermore,
electrophysiology studies have shown that the DDM may provide a plausible mechanism for
visual discrimination at the neuronal level (Gold & Shadlen, 2007). Finally, the DDM is more
parsimonious than many of the alternatives, as it uses fewer terms. This is an important feature
of a model, as a higher number of parameters leads to more degrees of freedom and may thus
cause over-fitting of the data. Nonetheless, since an important feature of a successful model is
biological feasibility, future studies at the neural level will provide useful evidence for
determining which model best describes value-based decision mechanisms in the brain.
Conclusion & Future Directions
This study explored a novel behavioral and conceptual framework that could inform
research on the neural substrates of voting choices, as well as provide a basis for developing
mathematical models of value-based choice that integrate beliefs gathered prior to the choice
with online evidence gathering during the trial. There are uncertainties and limitations inherent
to our design which should be addressed in future studies. As mentioned in the introduction,
the binary choice task is a good starting point for comparing political choice to economic
choice because it has been used widely in the past and provides the simplest possible choice.
However, while binary choice is a good approximation of some classes of economic choices, it
may not closely mimic the informationally complex choices involved in political behavior. In
order to eventually reach an explanatory level that is useful in political science, future work
must adapt the tasks used here in order to increase complexity. For example, future studies
could start with the most basic version of the task reported here (binary choice modeled by
K R A S T E V | 59
aDDM in non-partisans, with no party information) and increase complexity by adding other
elements of interest such as political issues, a full range of political parties, known candidates
and appearance ratings based on more factors.
In this study, we have shown that participants exhibit similar choice behavior and eye
movement patterns when making simple political choices to those observed in economic choice
in the past. We have also shown that although party information strongly influences decision
behavior in partisans, it does not do so in a way that completely supersedes other sources of
information. Thus, models of evidence gathering that drive a decision value toward a barrier may
generalize to more complex informational environments.
The work presented here provides a starting point for studying political choice in the
brain. Past work (Lim et al., 2011) has linked fixation-guided valuation to activity in the
vmPFC and striatum, suggesting that these areas are responsible for encoding fixationdependent relative value signals. Future work using neuroimaging or lesion methods with
similar task designs will be important in establishing the neural basis of political choice.
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CHAPTER 4: GENERAL DISCUSSION & CONCLUSION
This thesis made initial steps in investigating the conceptual, behavioral and neural
intersections of economic and political decision-making. Progress toward understanding the
decision processes behind political choice is not only relevant to political scientists, but is a step
toward broadening the scope of decision neuroscience. Research in neuroeconomics provides a
useful starting point for exploring the differences and commonalities between economic and
political decision-making (Cacioppo & Visser, 2003). As a first step, here we sought to
understand to what extent the fields of neuroeconomics and neuropolitics have taken comparable
experimental approaches, and whether these have yielded similar results.
We addressed this in Chapter 2 with a systematic review of the neuropolitics literature to
date. We found that studies of political choice have been little influenced by the neuroeconomics
literature, or the related and much broader animal literature on the brain basis of reward
processing and value-based learning. Whereas studies of economic choice have had a strong
experimental emphasis on tasks linking specific decisions to immediate reward outcomes, the
same is not possible in a political choice context as the rewards of political choice are by nature
ambiguous and time-delayed. Although none of the neuropolitics studies explicitly discussed
reward, we examined the results of the subset of neuropolitics studies which used fMRI to report
the neural correlates of positive and negative subjective value, looking for common patterns. We
found very little commonality between the foci reported by neuropolitics studies and the regions
of interest commonly related to subjective value in economic paradigms (Bartra at al., 2013).
Although this result is limited by the small number of studies, it leads to two provisional
conclusions: first, the neuropolitics literature to date has not taken advantage of frameworks
developed to study economic choice in the brain; second, the neural correlates and perhaps
K R A S T E V | 61
decision processes associated with political choice may be in part or entirely distinct from those
observed in economic choice.
Chapter 3 reports an effort to develop an experimental paradigm that bridges the study of
economic and political choice. A binary choice task commonly used to study economic choice
(Krajbich et al., 2010; Milosavljevic et al., 2010; Lim et al., 2011; Krajbich & Rangel, 2011;
Krajbich et al., 2012) was adapted for the canonical political choice central to Western
democracy: voting
for
political candidates.
This allowed
preliminary investigation of
information gathering and deliberation during voting, as indexed by choice behavior and eye
movements. Given that the fMRI meta-analysis reported in Chapter 2 did not yield overlapping
regions of activation for economic and political choice, we aimed to establish whether choice
behavior and eye movements followed similar patterns in experimental political choice as
previously reported for ‘deliberative’ economic choice. We also explored whether these
measures would reveal evidence of heuristic rather than deliberative processes under certain
conditions, by studying the effects of partisanship in the presence and absence of party
information. The deliberative-heuristic dichotomy is a central topic in the study of political
behavior (Schaffner & Strebb, 2002). These two “types” of decision-making (assuming their
conceptual distinction is legitimate) could potentially have distinct neural (Volz et al., 2006 &
2010), behavioral and eye movement correlates.
Choice behavior and eye movement patterns from the first condition conformed to those
seen in past studies of economic choice, providing preliminary evidence that deliberative
political choice likely relies on a similar information gathering process as deliberative economic
choice (Krajbich et al., 2010). Moreover, data showed that the partisan group made significantly
fewer visual fixations, suggesting that, while they seemed to be engaging the same decision
K R A S T E V | 62
processes as indexed by their overall patterns, they were systematically different in the amount
of information needed to reach a decision. We addressed this formally with a preliminary
modeling exercise, finding that the difference between the partisans and non-partisans could be
approximated by a lowered decision value barrier (the amount of evidence in support of an
option required before that option is chosen) within the aDDM.
Choice behavior and eye movement pattern data from the second condition allowed us to
test the effects of partisanship on political choice. Past work on political decision-making has
characterized the partisanship heuristic as particularly prevalent in low-information elections,
such as the one we created by providing nothing more than the visual appearance of candidates
(Schaffner & Strebb, 2002). Thus, we expected party information to completely supersede visual
appearance ratings in partisans. However, the data told a different story – although partisans
were highly affected by party information, they exhibited the same choice behavior and eye
movement patterns reported in economic paradigms thought to engage deliberative decisionmaking. Partisans took information conveyed by visual appearance into account and their
choices were significantly affected by attention, as indexed by eye gaze. Although further work
is needed to generalize these findings beyond Liberal partisans, these preliminary findings raise
the possibility that “heuristic” choice differs in extent, rather than in kind, from deliberative
choice. The behavior in both conditions, in both groups, could be accommodated by varying the
parameters of the aDDM, for example. This is as a key future direction to pursue, of importance
in both neuroeconomics and neuropolitics. In particular, our findings suggest that the framework
used to study economic choice in the past, including but not limited to imaging studies, is
applicable to political choice, as long as factors that are notably relevant in political choice, such
as partisanship, are addressed in the design.
K R A S T E V | 63
As studies of decision-making in the brain explore a growing number of ecologically
valid components of choice (for example: risk (Knutson et al., 2008), uncertainty (Tobler et al.,
2007), time-discounting (Kable & Glimcher, 2007), motivation (Bjork et al., 2010) and social
interactions (Fliessbach et al., 2007)), they become increasingly relevant to the study of political
choice, which incorporates many of these elements. However, if findings from such studies are
to be used to grow our understanding of political choice, then future work must first characterize
the exact role that these elements play in various kinds of political choice. The present thesis
provides a framework that may be useful in understanding the roles of evidence gathering and
heuristics in political choice. Furthermore, it attempts to lay the groundwork for a more
systematic investigation of political choice that leverages the wealth of existing research and
methodology present in neuroeconomics. Future work should aim to further bridge the gap
between neuropolitics and neuroeconomics as both fields likely study decision behaviors that
differ in degree rather than kind. These efforts have the potential to expand the explanatory limits
of neuroeconomics, and are a valuable step toward gaining a deeper understanding of political
choice which is, after all, a key driver of personal and social well-being.
K R A S T E V | 64
ACKNOWLEDGEMENTS
First and foremost, I would like to thank my supervisor Lesley Fellows for her continuous
support, wisdom and patience in the creation of this thesis as well as in the ideation and
execution of the associated experimental and conceptual work. Furthermore, I would like to
thank the members of my advisory committee, Alain Dagher and Dietlind Stolle, as well as
Elisabeth Gidengil, for their meaningful contribution throughout my degree.
Next, I would like
to express my gratitude to Avinash Vaidya for his constant aid at the lab and in the testing room
and to Christine Déry for her help in recruiting research participants. In addition, I am thankful to
past and present members of my lab (in alphabetical order, by last name) – Mathias Faeth, Ana
Fernandez Cruz, Eldad Hochman, Anne Löffler, Alison Simioni, Ami Tsuchida and Chenjie Xia
– for their assistance in my research and their feedback on presentations, conference materials,
publication submissions and this thesis. Finally I would like to thank my mentor, Josephine
Nalbantoglu, for her guidance.
This research was supported by a grant from CIHR (MOP: 97821) and the Fonds de recherche
du Québec-Société et culture.
K R A S T E V | 65
REFERENCES
Amodio D.M., Jost J.T., Master S.L., Yee C.M. (2007) “Neurocognitive correlates of
liberalism and conservatism.” Nature Neuroscience 10: 1246 – 1247.
Badre D., Poldrack R.A., Pare-Blagoev E.J., Insler R.Z., Wagner A.D. (2005) “Dissociable
controlled retrieval and generalized selection mechanisms in ventrolateral prefrontal
cortex.” Neuron. 47: 907–918.
Bartra O., McGuire J.T., Kable J.W. “The valuation system: A coordinate-based meta-analysis of
BOLD fMRI experiments examining neural correlates of subjective value.” (2013)
NeuroImage available online.
Bechara A., Damasio A. R., Damasio H., & Anderson S.W. “Insensitivity to future
consequences following damage to human prefrontal cortex.” (1994) Cognition 50(13): 7-15.
Berns G.S., Bell E., Capra M.C., Prietula M.J., Moore S., Anderson B., Ginges J., Atran S.
(2012) “The price of your soul: neural evidence for the non-utilitarian representation of
sacred values.” Philosophical Transactions B 370(1662).
Bjork J.M., Smith A.R., Chen G., Hommer D.W. (2010) “Adolescents, adults and rewards:
Comparing motivational neurocircuitry recruitment using fMRI.” PloS One. 5:e11440.
Busemeyer J.R., Townsend J.T. (1993) “Decision field theory: a dynamic-cognitive approach to
decision-making in an uncertain environment.” Psychological Review 100(3): 432.
Cacioppo, J.T. & Visser, P.S. (2003) “Political psychology and social neuroscience: strange
bedfellows or comrades in arms?” Political Psychology 24(4): 647-656.
K R A S T E V | 66
Camille N., Tsuchid, A., Fellows L.K. (2011) “Double dissociation of stimulus-value and actionvalue learning in humans with orbitofrontal or anterior cingulate cortex damage.” The
Journal of Neuroscience 31(42), 15048–15052.
Carney D.R., Jost J.T., Gosling S.D., Potter J. (2008) “The Secret Lives of Liberals and
Conservatives: Personality Profiles, Interaction Styles, and the Things They Leave
Behind.” Political Psychology 29(6): 807-840.
Cottam M.L., Dietz-Uhler B., Mastors E., Preston T. (2010) “Introduction To Political
Psychology.” New York, NY: Psychology Press.
Cunningham W.A., Raye C.L., Johnson M.K. (2004) “Implicit and explicit evaluation: FMRI
correlates of valence, emotional intensity, and control in the processing of attitudes.”
Journal of Cognitive Neuroscience 16(10):1717-29.
Dawes C.T. & Fowler J.H. (2009) “Partisanship, Voting, and the Dopamine D2 Receptor Gene.”
The Journal of Politics 71: 1157-1171.
De Martino B., Kumaran D., Seymour B., Dolan R.J. (2006) “Frames, Biases and Rational
Decision-Making in the Human Brain.” Science 313(5787): 684-687.
Delli Carpini M.X. & Keeter S.. (1996) “What Americans Know About Politics and Why it
Matters.” New Haven: Yale University Press.
Dhont K., Van Hiel A., Pattyn S., Onraet E., Severens E. (2011) “A step into the anarchist’s
mind: examining political attitudes and ideology through event-related brain potentials.”
Social Cognition and Affective Neuroscience 7(3): 296-303.
K R A S T E V | 67
Downs A. (1957) “An Economic Theory of Democracy.” New York: Harper and Row.
Fellows L.K. (2004) “The cognitive neuroscience of human decision-making: a review and
conceptual framework.” Behavioral and Cognitive Neuroscience Reviews 3: 159–72.
Fellows L.K. (2007) “The Role of Orbitofrontal Cortex in Decision-making.” Annals of the New
York Academy of Sciences 1121: 421-430.
Fiorina M.P. (1981) “Retrospective Voting in American National Elections.” New Haven: Yale
University Press.
Fiske S.T. & Taylor S.E. (1984) “Social Cognition.” New York: Random House.
Fliessbach K., Weber B., Trautner P., Dohmen T., Sunde U., Elger C.E., Falk A. (2007) “Social
comparison affects reward-related brain activity in the human ventral striatum.” Science.
318:1305-1308.
Fowler J.H. & Schreiber D. (2008) “Biology, Politics and the Emerging Science of Human
Nature.” Science 322(5903): 912-914.
Friend J.M. & Thayer B.A. (2011) “Brain Imaging and Political Behavior.” Research in
Biopolitics 9: 231-255.
Gigerenzer G.,
Gaissmaier W.
(2011) “Heuristic Decision-making.” Annual Review of
Psychology 62: 451-482.
Gold J.I., Shadlen M.N. (2007) “The neural basis of decision-making.” Annu Rev Neurosci
30: 535-574.
Goldstein D.G., Gigerenzer G. (2002) “Models of Ecological Rationality: The Recognition
Heuristic.” Psychological Review 109(1): 75-90.
K R A S T E V | 68
Gozzi M., Zamboni G., Krueger F., Grafman J. (2010) “Interest in politics modulates neural
activity in the amygdala and ventral striatum.” Hum. Brain Mapp 31: 1763–1771.
Green, D.P. & Shapiro, I. (1994) “Pathologies of rational choice theory: a critique of applications
in political science.” Canadian Journal of Political Science. 28(2): 373-374.
Gutman A. & Thompson D. (2002) “Why Deliberative Democracy?” Princeton University Press.
Hauser, J. (2011) “A marketing science perspective on recognition-based heuristics (and the
fast-and-frugal paradigm).” Judgment and Decision- making 6(5): 396-408.
Henri-Bhargava A., Simioni A., Fellows L.K. (2012) “Ventromedial frontal lobe damage
disrupts the accuracy, but not the speed, of value-based preference judgments.”
Neuropsychologia 50(7): 1536 – 1542.
Huckfeldt R., Mondak J.J., Craw M., Mendez J.M. (2005) “Making sense of candidates:
Partisanship, ideology, and issues as guides to judgment.” Cognitive Brain Research
23(1):11-23.
Johnson E.J., Ratcliff R. (2014) “Computational and Process Models of Decision-making in
Psychology and Behavioral Economics.” Neuroeconomics: Decision-making and the
Brain. Academic Press 35-47.
Jost J.T., Amodio D.M. (2012) “Political ideology as motivated social cognition: Behavioral
and neuroscientific evidence.” Motivation and Emotion 36(1): 55-64.
Kable J.W., Glimcher P.W. (2007) “The neural correlates of subjective value during
intertemporal choice.” Nat Neurosci 10(12):1625-33.
K R A S T E V | 69
Kable J.W.,
Glimcher P.W.
(2009) “The Neurobiology of Decision: Consensus and
Controversy.” Neuron 63(6): 733-745.
Kanai R., Feilden T., Firth C., Rees G. (2011) “Political Orientations Are Correlated with
Brain Structure in Young Adults.” Current biology 21(8): 677 - 680.
Kaplan J.T., Freedman J., Iacoboni M. (2007) “Us versus them: Political attitudes and party
affiliation
influence
neural
response
to
faces
of
presidential
candidates.”
Neuropsychologia 45:55–64.
Kato J., Ide H., Kabashima I., Kadota H., Takano K., Kansaku K. (2009) “Neural Correlates of
Attitude Change
Following Positive and
Negative Advertisements.” Front Behav
Neurosci 3: 6.
Knutson B., Wimmer G.E., Kuhnen C.M., Winkielman P. (2008) “Nucleus accumbens
activation mediates the influence of reward cues on financial risk taking.” NeuroReport.
19:509-513.
Knutson K.M., Wood J.N., Spampinato M.V., Grafman J. (2006) “Politics on the Brain: An
fMRI Investigation.” Soc Neurosci 1(1): 25–40.
Krajbich I., Armel C., Rangel A. (2010) “Visual fixations and the computation and comparison
of value in simple choice.” Nature Neurosci 13: 1292-1298.
Krajbich I., Lu D., Camerer C., Rangel A. (2012) “The attentional drift-diffusion model
extends to simple purchasing decisions.” Frontiers in Psychology 3:193.
K R A S T E V | 70
Krajbich I., Oud B., Fehr E. (2014) “Benefits of neuroeconomics modeling: New policy
interventions and predictors of preference.” American Economic Review: Papers &
Proceedings 104(5): 501-506
Krajbich I., Rangel A. (2011) “Multialternative drift-diffusion model predicts the relationship
between visual fixations and choice in value-based decisions.” PNAS
108: 13852-
13857.
Lau R.R., Redlawsk D.P. (2001) “Advantages and Disadvantages of Cognitive Heuristics in
Political Decision- making.” American Journal of Political Science. 45(4): 951-971.
Lieberman M.D., Schreiber D., Ochsner K.N. (2003) “Is political cognition like riding a
bicycle? How cognitive neuroscience can inform research on political thinking.”
Political Psychology 24: 681-704.
Lim S.L., O’Doherty J.P., Rangel A. (2011) “The Decision Value Computations in the vmPFC
and Striatum Use a Relative Value Code That is Guided by Visual Attention.” The
Journal of Neuroscience 31: 13214-13223.
Lin A., Adolphs R., Rangel A. (2012) “Social and monetary reward learning engage
overlapping neural substrates.” Soc Cogn Affect Neurosci 7(3): 274-281.
Lodge M., McGraw K.M., Stroh P. (1989) “An Impression-Driven Model of Candidate
Evaluation.” American Political Science Review 83:399-419.
Lupia A. (1994) “Shortcuts Versus Encyclopedias: Information and Voting Behavior in
California
Insurance
88(1):63-76.
Reform Elections.” The American Political Science Review
K R A S T E V | 71
Marcus G.E.,
Wood S.L., Theiss-Morse E. (1998) “Linking neuroscience to political
intolerance and political judgment.” Politics and the Life Sciences 17: 165-178.
Marcus G.E. (2000) “Emotions in politics.” Annual Review of Political Science 3: 221-250.
McDermott R. (2009) “The case for increasing dialogue between political science and
neuroscience.” Political Research Quarterly 62: 571-583.
Milosavljevic M., Malmaud J., Huth A., Koch C., Rangel A. (2010) “The Drift Diffusion
Model can account for the accuracy and reaction time of value-based choices under
high and low time pressure.” Judgment and Decision- making 5: 437-449.
Newcombe
R.G.
(1998)
“Two-Sided
Confidence Intervals for the Single Proportion:
Comparison of Seven Methods.” Statistics in Medicine 17: 857-872.
Oxley D.R., Smith K.B., Alford J.R., Hibbing M.V., Miller J.L., Scalora M., Hatemi P.K.,
Hibbing J.R. (2008) “Political Attitudes Vary with Physiological Traits.” Science
321(5896):1667–70.
Padoa-Schioppa C. (2011) “Neurobiology of Economic Choice: A Good-Based Model.” Annu
Rev Neurosci 34: 333-359.
Pärnamets P., Balkenius C., Richardson D.C. (2014) “Modelling moral choice as a diffusion
process dependent on visual fixations.” In Proceedings of the 36th Annual Conference of
the Cognitive Science Society 1132-1137.
Pegors T.K., Kable J.W., Chatterjee A., Epstein R.A. (2015) “Common and Unique
Representations in pFC for Face and Place Attractiveness.” Journal of Cognitive
Neuroscience 27(5): 959-973.
K R A S T E V | 72
Phan K.L., Wager T., Taylor S.F., Liberzon I. (2002) “Functional neuroanatomy of emotion: a
meta-analysis of emotion activation studies in PET and fMRI.” Neuroimage
16 (2):
331–48.
Philiastides M.G., Ratcliff R. (2013) “Influence of Branding on Preference-Based Decisionmaking.” Psychological Science 24(7): 1208-1215.
Popkin S. (1991) “The Reasoning Voter.” Chicago: University of Chicago Press, 1991.
Rahn W.M. (1993) “The Role of Partisan Stereotypes in Information Processing about Political
Candidates.” American Journal of Political Science 37(2):472-496.
Rangel A., Camerer C., Montague P.R. (2008) “A Framework for Studying the Neurobiology
of Value-Based Decision-Making.” Nature Review Neuroscience 9: 545-556.
Rangel, A. & Clithero, J. (2013) “The computation of stimulus values in simple choice.”
Neuroeconomics: Decision Making and the Brain, 2 nd ed. 125-148.
Ratcliff R. (1978) “A theory of memory retrieval.” Psychological Review 85:59–108.
Rogers T., Fox C.R., Gerber A.S. (2012) “Rethinking Why People Vote: Voting as Dynamic
Social Expression.” Behavioral Foundations of Policy. Ed. Eldar Shafir. Princeton
University Press 91-107.
Rule N.O., Freeman J.B., Moran J.M., Gabrieli J.D.E., Adams Jr R.B., Ambady N. (2010)
“Voting behavior is reflected in amygdala response across cultures.” Soc Cogn Affect
Neurosci 5(2-3): 349-355.
Schaffner B. & Streb M.J. (2002) “The Partisan Heuristic in Low-Information Elections.”
Public Opinion Quarterly. 66(4): 559-581.
K R A S T E V | 73
Schreiber D., Fonzo G., Simmons A.N., Dawes C.T., Flagan T., Fowler J.H., Paulus M.P.
(2014) “Red Brain, Blue Brain: Evaluative Processes Differ in Democrats and Republicans.”
PLoS ONE 8(2): e52970.
Schreiber
D.
(2004) “Political Cognition as Social Cognition: Are We
All Political
Sophisticates?” Prepared for The Political Dynamics of Thinking and Feeling.
Smith P.L., Ratcliff R. (2004) “Psychology and neurobiology of simple decisions.” Trends in
Neuroscience 27:161–168.
Smith S.M., Jenkinson M., Woolrich M.W., Beckmann C.F., Behrens T.E.J., Johansen-Berg
H., Bannister P.R., De Luca M., Drobnjak I., Flitney D.E., Niazy R., Saunders J.,
Vickers J., Zhang Y., De Stefano N., Brady J.M., Matthews P.M. (2004) “Advances in
functional and structural MR image analysis and implementation as FSL.” NeuroImage
23(S1):208-219.
Sniderman P.M., Brody R.A., Tetlock P.E. (1993) “Reasoning and Choice: Explorations in
Political Psychology.” Cambridge University Press.
Spezio M.L. & Adolphs R. (2007) “Emotional processing and political judgment. Toward
integrating political psychology and neuroeconomics.” In The affect effect: Dynamics
of emotion in political thinking and behavior, Edited by: Neuman, W.R., Marcus, G.E.,
Crigler, A.N. and MacKuen, M. 71–96. Chicago: University of Chicago Press.
Spezio M.L., Rangel A., Alvarez R.M., O’Doherty J.P., Mattes K., Todorov A., Kim H.,
Adolphs R. (2008) “A neural basis for the effect of candidate appearance on election
outcomes.” Soc Cogn Affect Neurosci 3(4): 344-352.
K R A S T E V | 74
Theodoridis A.G. & Nelson A.J. (2012) “Of BOLD Claims and Excessive Fears: A Call for
Caution and Patience Regarding Political Neuroscience.”
Political Psychology.
33(1):
27–43.
Tingley D. (2006) “Neurological imaging as evidence in political science: a review, critique,
and guiding assessment.” Social Science Information. 45(1): 5-33.
Tobler P.N., O'Doherty J.P., Dolan R.J., Schultz W. (2007) “Reward value coding distinct
from risk attitude-related uncertainty coding in human reward systems.” Journal of
Neurophysiology 97:1621-1632.
Todorov A, Mandisodza AN, Goren A, Hall CC. (2005) “Inferences of competence from faces
predict election outcomes.” Science 308(5728): 1623-1626.
Tusche A., Kahnt T., Wisniewski D., Haynes J.D. (2013) “Automatic processing of political
preferences in the human brain.” NeuroImage 72: 174–182.
Usher M., McClelland J.L. (2001) “The time course of perceptual choice: the leaky, competing
accumulator model.” Psychological Review 108(3): 550.
Vickers D. (1970) “Evidence for an accumulator model of psychophysical discrimination.”
Ergonomics 13(1): 37–58.
Volz K.G., Schooler L.J., von Cramon D.Y. (2010) “It just felt right: The neural correlates of
the fluency heuristic.” Consciousness and Cognition 19(3): 829-837.
Volz K.G., Schooler L.J., Schubotz R.I., Raab M., Gigerenzer G., von Cramon D.Y. (2006)
“Why you think Milan is larger than Modena: Neural correlates of the recognition
heuristic.” Journal of Cognitive Neuroscience 18(11): 1924-1936.
K R A S T E V | 75
Watson K.K., Platt M.L. (2012) “Social Signals in Primate Orbitofrontal Cortex.” Current
Biology. 22(23): 2268 – 2273.
Westen D., Blagov P.S., Harenski K., Kilts C., Hamann S. (2006) “Neural bases of motivated
reasoning: an FMRI study of emotional constraints on partisan political judgment in the
2004 U.S. Presidential election.” J Cogn Neurosci 1947-58.
Wilson E.B. (1927) “Probable Inference, the Law of Succession, and Statistical Inference.”
Journal of the American Statistical Association 22: 209-212.
Wright P., He G., Shapira N.A., Goodman W.K., Liu Y. (2004) “Disgust and the insula: fMRI
responses to pictures of mutilation and contamination. ” Neuroreport 15(15): 2347-2351.
Zamboni G., Gozzi M., Krueger F., Duhamel J., Sirigu A., Grafman J. (2009) “Individualism,
conservatism, and radicalism as criteria for processing political beliefs: A parametric
fMRI study.” Social Neuroscience 4(5): 367-383.